Social franchising: whatever happened to old-fashioned notions of evidence-based practice?
Bibliographic record
Abstract
Social franchising, which draws on principles of commercial franchising for achieving socially beneficial ends, is based on the premise that organising private sector health-care providers under a standardised, branded set of services will improve the quality and accessibility of services. The popularity of the idea can be gauged by its rapid growth worldwide: the number of social franchises have doubled every 4 years since 1994, and, by 2015, more than 90 such programmes existed in 40 low-income and middle-income countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.446 | 0.528 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.007 | 0.121 |
| Scholarly communication | 0.030 | 0.072 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.026 | 0.056 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".